IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i2p659-d1568147.html
   My bibliography  Save this article

The Impact of Intelligent Logistics on Logistics Performance Improvement

Author

Listed:
  • Aishan Ye

    (Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China
    College of Yonyou Digital & Intelligence, Nantong Institute of Technology, Nantong 226000, China)

  • Jiayi Cai

    (College of Yonyou Digital & Intelligence, Nantong Institute of Technology, Nantong 226000, China)

  • Zhenjie Yang

    (Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China)

  • Yangyang Deng

    (College of Yonyou Digital & Intelligence, Nantong Institute of Technology, Nantong 226000, China)

  • Xiaohua Li

    (College of Yonyou Digital & Intelligence, Nantong Institute of Technology, Nantong 226000, China)

Abstract

The logistics industry is essential to global economic development but continues to grapple with challenges related to quality improvement, cost reduction, and efficiency enhancement. Addressing these issues is crucial for promoting high-quality growth within the sector. The emergence of intelligent logistics—leveraging automation, data analytics, and Internet of Things (IoT) technologies—offers a promising approach to transforming traditional logistics operations. This study develops a theoretical framework that integrates these intelligent logistics components to investigate their mechanisms and limitations in influencing logistics performance. Using an empirical analysis of Chinese provincial panel data, we identify significant disparities in logistics industry performance across the provinces, with most regions exhibiting an initial improvement followed by a subsequent decline. Our findings reveal a notable spatial interaction effect between intelligent logistics and logistics performance, indicating that intelligent logistics substantially enhance performance. However, the impact varies by region: it significantly promotes performance in the eastern and western regions but has a limited effect in the central and northeastern regions, potentially due to distortions in production factors and other regional specificities. Additionally, the degree of openness to the outside world positively influences logistics performance in the western region. The proposed mechanisms are validated in all regions except the eastern region. This study provides valuable insights for policymakers on leveraging intelligent logistics to improve logistics industry performance, highlighting the need for region-specific strategies to maximize the benefits of intelligent logistics technologies.

Suggested Citation

  • Aishan Ye & Jiayi Cai & Zhenjie Yang & Yangyang Deng & Xiaohua Li, 2025. "The Impact of Intelligent Logistics on Logistics Performance Improvement," Sustainability, MDPI, vol. 17(2), pages 1-19, January.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:659-:d:1568147
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/2/659/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/2/659/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weidong Jiang & Naiwen Li, 2024. "The Intelligent Upgrading of Logistics between an Internet Enterprise and a Logistics Enterprise Based on Differential Game Theory," Sustainability, MDPI, vol. 16(19), pages 1-22, October.
    2. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    3. Cui, Huixia & Chen, Xiangyong & Guo, Ming & Jiao, Yang & Cao, Jinde & Qiu, Jianlong, 2023. "A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Genz, Sabrina & Schnabel, Claus, 2023. "Digitalization is not gender-neutral," Economics Letters, Elsevier, vol. 230(C).
    2. Barth, Erling & Davis, James C. & Freeman, Richard B. & McElheran, Kristina, 2023. "Twisting the demand curve: Digitalization and the older workforce," Journal of Econometrics, Elsevier, vol. 233(2), pages 443-467.
    3. Radu Vranceanu & Angela Sutan, 2023. "Should the firm or the employee pay for upskilling? A contract theory approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 197-207, January.
    4. Liu, Shasha & Wu, Yuhuan & Kong, Gaowen, 2024. "Politics and Robots," International Review of Financial Analysis, Elsevier, vol. 91(C).
    5. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    6. Samuel Muehlemann, 2024. "AI Adoption and Workplace Training," Economics of Education Working Paper Series 0232, University of Zurich, Department of Business Administration (IBW).
    7. Juan F. Jimeno, 2019. "Fewer babies and more robots: economic growth in a new era of demographic and technological changes," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(2), pages 93-114, June.
    8. Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2022. "The empirics of technology, employment and occupations: lessons learned and challenges ahead," DISCE - Working Papers del Dipartimento di Politica Economica dipe0028, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    9. Bárány, Zsófia L. & Siegel, Christian, 2020. "Biased technological change and employment reallocation," Labour Economics, Elsevier, vol. 67(C).
    10. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    11. Heyman, Fredrik & Olsson, Martin, 2022. "Long-Run Effects of Technological Change: The Impact of Automation and Robots on Intergenerational Mobility," Working Paper Series 1451, Research Institute of Industrial Economics, revised 29 Jun 2023.
    12. Armanda Cetrulo & Giovanni Dosi & Angelo Moro & Linnea Nelli & Maria Enrica Virgillito, 2023. "Automation, digitalization and decarbonization in the European automotive industry: a roadmap towards a just transition," LEM Papers Series 2023/36, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Gartner, Hermann & Stüber, Heiko, 2019. "Strukturwandel am Arbeitsmarkt seit den 70er Jahren: Arbeitsplatzverluste werden durch neue Arbeitsplätze immer wieder ausgeglichen (Structural change on the labor market since the 70s : The destructi," IAB-Kurzbericht 201913, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    14. Lionel Fontagné & Ariell Reshef & Gianluca Santoni & Giulio Vannelli, 2024. "Automation, global value chains and functional specialization," Review of International Economics, Wiley Blackwell, vol. 32(2), pages 662-691, May.
    15. Janine Berg & Francis Green & Laura Nurski & David A Spencer, 2023. "Risks to job quality from digital technologies: Are industrial relations in Europe ready for the challenge?," European Journal of Industrial Relations, , vol. 29(4), pages 347-365, December.
    16. Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers 13606, Institute of Labor Economics (IZA).
    17. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    18. Dilip Mookherjee & Debraj Ray, 2022. "Growth, Automation and the Long-Run Share of Labor," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 46, pages 1-26, October.
    19. Nicoletta Corrocher & Daniele Moschella & Jacopo Staccioli & Marco Vivarelli, 2024. "Innovation and the labor market: theory, evidence, and challenges," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 33(3), pages 519-540.
    20. Lee Ohanian & Musa Orak & Shihan Shen, 2023. "Revisiting Capital-Skill Complementarity, Inequality, and Labor Share," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 479-505, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:659-:d:1568147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.